catalog_gwas_pliercanon_sep_ldclumping

Last updated: 2020-11-23

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Introduction

In this part, I considered the traits separately. I selected the SNPs with pval < 5e-8 for each traits. Then, I did LD Clumping for these SNPs to eliminate the LD and select a smaller subset of SNPs. After that, I did association tests for the plier_canonical factors with the SNPs. I also did colozalization analysis for some significant factor~snp pairs.

Material and Methods

Catalog GWAS data:

Platelet count, white blood cell count, myeloid white cell count, lymphocyte counts, red blood cell count, granulocyte count, eosinophil count, neutrophil count from Astle WJ, Elding H, Jiang T, et al. The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease. Cell. 2016;167(5):1415-1429.e19. doi:10.1016/j.cell.2016.10.042.
T2D. I first used data from our lab collaction Morris et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet. 2012 Sep;44(9):981-90. doi: 10.1038/ng.2383. Epub 2012 Aug 12. PMID: 22885922; PMCID: PMC3442244. but it doesn’t contain MAF info of variants.So I added this from GWAS Catalog: Wood AR et al. Variants in the FTO and CDKAL1 loci have recessive effects on risk of obesity and type 2 diabetes, respectively. Diabetologia. 2016 Jun;59(6):1214-21. doi: 10.1007/s00125-016-3908-5. Epub 2016 Mar 10. PMID: 26961502; PMCID: PMC4869698..
Asthma. I first used data from our lab collaction Zhu et al, Shared Genetics of Asthma and Mental Health Disorders: A Large-Scale Genome-Wide Cross-Trait Analysis. European Respiratory Journal, 2019 (PMID: 31619474) but it doesn’t contain MAF info of variants.So I added this from GWAS Catalog: Manuel A.R. et al. Genetic Architectures of Childhood- and Adult-Onset Asthma Are Partly Distinct,The American Journal of Human Genetics,Volume 104, Issue 4,2019,Pages 665-684,ISSN 0002-9297,https://doi.org/10.1016/j.ajhg.2019.02.022..
IBD,Ulcerative colitist,Crohn’s disease data are from lab collection Liu, van Sommeren et al, Nature Genetics, 2015
Waist-hip ratio data are from Shungin et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature. 2015 Feb 12;518(7538):187-196. doi: 10.1038/nature14132. PMID: 25673412; PMCID: PMC4338562.
BMI data are also from lab collection: Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, Powell C, Vedantam S, Buchkovich ML, Yang J, Croteau-Chonka DC, Esko T et al. (2015). Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197-206
HDL & LDL data are downloaded from GWAS Catalog. Global Lipids Genetics Consortium., Willer, C., Schmidt, E. et al. Discovery and refinement of loci associated with lipid levels. Nat Genet 45, 12741283 (2013). https://doi.org/10.1038/ng.2797

Filtered the SNPs using pval < 5e-8 as cut off from the GWAS Catalog data.
Did LD Clumping for the SNPs in step 2. The PLINK LD Clumping patameters are:

–clump-p1 0.0001 Significance threshold for index SNPs

–clump-p2 0.01 Secondary significance threshold for clumped SNPs

–clump-r2 0.1 LD threshold for clumping

–clump-kb 1000 Physical distance threshold for clumping

For each trait, I got a subset of SNPs that are not in LD with each other.

Did association tests for plier_canonical factors and SNPs in 3. The association tests were corrected by 1)10 genotype PCs of whole genome; 2)10 PCs + GTEx:Sequencing platform,Sequencing protocol,Sex; 3)10 PCs + GTEx:Sequencing platform,Sequencing protocol,Sex + AGE
For each trait, I made a plot of association with LV(indicating by beta in GWAS) vs association with trait(indicating by ln(odds ratio) or beta in GWAS) to show if the variants have the correlated effect direction. The effect sizes of Catalog GWAS and factor association tests are harmonized by TwoSampleMR R package to make the effect alleles in these two analysis identical. The LVs have more than one significant SNPs with FDR<0.2 are included in the plotting.Besides, for each plots, I fitted the points with intercept = 0. The pvalues and r-squared are shown on the plots.
For the traits and LVs in 5, I made an info table to show more details of the SNPs.
For several LVs we are interested in, I did gene set enrichment analysis to test if the LVs are correlated with some KEGG/REACTOME pathways. I used two kind gene sets to do GSEA: 1. genes that used to compute LVs; 2. Sorting the genes in 1 by their loadings, take the top 25% as the gene set. For both gene sets, the gene scores used as input of GSEA are the gene loadings.
Resampling. For some promising trait-factor pairs, I did resampling. I resampled the SNPs without replacement, I fitted the points with intercept = 0 again and recorded the pvalues and r-squared. The resampling was repeated 1000 times. The following plots are the resampling results.

Snps after filter

After filtering by ‘pval < 5e-8’ and LD Clumping, for each trait, I got :

platelet count trait contains 688 SNPs with pval<5e-8.

white blood cell count trait contains 368 SNPs with pval<5e-8.

myeloid white cell count trait contains 319 SNPs with pval<5e-8.

lymphocyte count trait contains 436 SNPs with pval<5e-8.

red blood cell count trait contains 466 SNPs with pval<5e-8.

granulocyte count trait contains 316 SNPs with pval<5e-8.

eosinophil count trait contains 491 SNPs with pval<5e-8.

neutrophil count trait contains 317 SNPs with pval<5e-8.

IBD trait contains 116 SNPs with pval<5e-8.

Ulcerative colitist trait contains 73 SNPs with pval<5e-8.

Crohn’s disease trait contains 96 SNPs with pval<5e-8.

BMI trait contains 104 SNPs with pval<5e-8.

T2D contains 14 SNPs with pval<5e-8. T2D_2 contains 4 SNPs with pval<5e-8.

Asthma trait contains 186 SNPs with pval<5e-8. Asthma_2 trait contains 112 SNPs with pval<5e-8.

HDL trait contains 227 SNPs with pval<5e-8.

LDL trait contains 204 SNPs with pval<5e-8.

WHR trait contains 36 SNPs with pval<5e-8.

Results - pval < 5e-8 & association test covariants: 10 PCs + GTEx:Sequencing platform,Sequencing protocol,Sex + AGE

Summary table

I used ‘qvalue’ R package to compute the fdr from p-values for each SNP and made a table to show the number of SNPs that pass the threshold. The thresholds are ‘fdr < 0.1’,‘fdr < 0.2’,‘pval < 5e-8’. The ‘num_significant_pairs’ indicates the number of significant pairs under each threshold. If a trait~factor pair has as least 1 significant SNP, we named it as ‘significant pair’.

Info table

For each trait, I made a table to show the info of snps with fdr>0.2 in the factor ~ SNP + genotype pcs association test. For each trait,The LVs have more than one significant SNPs with FDR<0.2 are included.

The suffix ’_assoc’ here means that results are from factor ~ SNP + genotype pcs association test. The suffix ’_gwas’ here means results are from original GWAS results files. For EUR.CD, EUR.IBD, EUR.UC,T2D, asthma, the effectsize_gwas here means ‘ln(OR)’, for others, it means ‘beta’.

‘snp_ld’ here means the snps that in LD with the snp in each line.’ld_r2’ means the LD r-squared which is corresponding to the ‘snp_ld’ column. ‘cis-eqtl’ column indicates whether the snp is a cis-eqtl according to GTEx data. ‘cis_gene_hgnc’ and ‘cis_gene_hgnc’ is the genes that the snp influence when it act as cis-eqtl. ‘func’ and ‘func_gene’ are obtained from ANNOVAR, which indicating the snp function within the genes.

Enrichment analysis

For some promising trait-factor pairs (i.e. BMI-LV3, LV27, RBC-LV82, AsthmaLV68, WBC-LV119, Lymphocyte-LV23, LV78), I did enrichment analysis with WebGestalt. The analysis are under different settings:

ORA: For each factor, I sorted the genes that have non-zero loadings by their loadings, taking the top 25% as the gene set. The function database here are Reactome pathway; Disgenet + GLAD4U + OMIM disease dataset; geneontology biological process. The reference set affy hugene 2 0 st v1. Minimum number of genes for a category is 5, maximum number of genes for a category is 2000(default settings). All categories that have fdr<0.01 are listed.

BMI-LV27

BMI-LV76

BMI-LV90

PLT-LV49

RBC-LV82

Asthma-LV36

Asthma-LV39

WBC-LV6

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WBC-LV119

Lymphocyte-LV23

Lymphocyte-LV26

LDL-LV125

WHR

Effect size plots

For each trait, I made a plot of association with LV(indicating by beta in GWAS) vs association with trait(indicating by ln(odds ratio) or beta in GWAS) to show if the variants have the correlated effect direction. The effect sizes of Catalog GWAS and factor association tests are harmonized by TwoSampleMR R package to make the effect alleles in these two analysis identical. The LVs have more than one significant SNPs with FDR<0.2 are included in the plotting.Besides, for each plots, I fitted the points with intercept = 0. The pvalues and r-squared are shown on the plots.

I also relaxed the fdr threshold of the SNPs that used to make effect size plots(from 0.2 to 0.3/0.5)

Asthma_2

BMI

Eosinophil count

Crohn’s disease

IBD

Ulcerative colitist

LDL

granulocyte count

None of the LVs have >1 SNPs at FDR<0.2.

lymphocyte count

myeloid white cell count

neutrophil count

plt

rbc

T2D_2

T2D

asthma

wbc

WHR

Effect size plots - checking the reverse causality

To check if the effect size correlation is due to reverse causality: i.e. trait -> LV (trait causally affect LV), instead of LV -> trait (which is what we like to see). I used all SNPs associated with traits(pval<5E-8). The x-axis is the effects of these SNPs on trait, and y-axis is the effects on LV.

Some pair show p < 0.05, the result may be driven by the possible causal effect of LV -> trait. To test this, I removed the SNPs that are associated with LVs at FDR < 0.2 and made the plots again.

BMI

LDL

lymphocyte count

platelet count

red blood cell count

asthma

white blood cell count

WHR

QQplots

BMI

LV27, LV76 and LV90

LDL

LV125

platelet count

LV49

red blood cell count

LV82

asthma

LV36 and LV79

white blood cell count

LV6 and LV119

WHR

LV47

Colocalization – 200kb

The colocalization analysis was performed using the approximate Bayes factor test implemented in the Coloc package. Coloc computes five posterior probabilities (PP0, PP1, PP2, PP3 and PP4), each corresponding to a hypothesis: H0, no association with either trait; H1, association with trait 1 but not with trait 2; H2, association with trait 2 but not with trait 1; H3, association with trait 1 and trait 2, two independent SNPs; H4, association with trait 1 and trait 2, one shared SNP. We ran Coloc with the default parameters and used PP4 to assess evidence of colocalization. We visualized the colocalization of factor - QTLs and GWAS associations using the LocusCompareR package.

SNP selection: 1. Chose the SNPs in the info table. 2. For each SNP, the region used in colocalization analysis is between [pos-100kb, pos+100kb]. 3. All SNPs in this region are included in alalysis.

BMI

LV27

PPs in coloclization analysis
		note
nsnps	499	NA
PP.H0.abf	1.35039761367912e-145	no association with either trait
PP.H1.abf	7.86241579658514e-147	association with trait 1 but not with trait 2
PP.H2.abf	0.833675789922105	association with trait 2 but not with trait 1
PP.H3.abf	0.0484211763716254	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.117903033706277	association with trait 1 and trait 2, one shared SNP

PPs in coloclization analysis
		note
nsnps	458	NA
PP.H0.abf	1.00714955121074e-08	no association with either trait
PP.H1.abf	1.07025940120869e-09	association with trait 1 but not with trait 2
PP.H2.abf	0.841859094244127	association with trait 2 but not with trait 1
PP.H3.abf	0.0893924054203391	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.0687484891937805	association with trait 1 and trait 2, one shared SNP

LV76

PPs in coloclization analysis
		note
nsnps	750	NA
PP.H0.abf	0.0428105191769995	no association with either trait
PP.H1.abf	0.00481701109736574	association with trait 1 but not with trait 2
PP.H2.abf	0.811244208573467	association with trait 2 but not with trait 1
PP.H3.abf	0.0912307603958566	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.049897500756311	association with trait 1 and trait 2, one shared SNP

PPs in coloclization analysis
		note
nsnps	551	NA
PP.H0.abf	0.0427388368431629	no association with either trait
PP.H1.abf	0.0223982915556723	association with trait 1 but not with trait 2
PP.H2.abf	0.588272582852666	association with trait 2 but not with trait 1
PP.H3.abf	0.308259737393146	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.0383305513553527	association with trait 1 and trait 2, one shared SNP

lymphocyte count

LV26

pvalues in coloclization
		note
nsnps	1901	NA
PP.H0.abf	0.551400357412064	no association with either trait
PP.H1.abf	0.350390080527746	association with trait 1 but not with trait 2
PP.H2.abf	0.0485827272932968	association with trait 2 but not with trait 1
PP.H3.abf	0.0308533605382535	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.0187734742286402	association with trait 1 and trait 2, one shared SNP

pvalues in coloclization
		note
nsnps	1534	NA
PP.H0.abf	0.624231586783693	no association with either trait
PP.H1.abf	0.29691846185701	association with trait 1 but not with trait 2
PP.H2.abf	0.0454737736049874	association with trait 2 but not with trait 1
PP.H3.abf	0.0216180395193858	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.0117581382349238	association with trait 1 and trait 2, one shared SNP

platelet count

LV49

pvalues in coloclization
		note
nsnps	1532	NA
PP.H0.abf	0.00028384352554766	no association with either trait
PP.H1.abf	9.53409302315354e-05	association with trait 1 but not with trait 2
PP.H2.abf	0.721638825542442	association with trait 2 but not with trait 1
PP.H3.abf	0.242357492575299	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.0356244974264805	association with trait 1 and trait 2, one shared SNP

pvalues in coloclization
		note
nsnps	1370	NA
PP.H0.abf	1.8417589323712e-05	no association with either trait
PP.H1.abf	4.40424788747121e-05	association with trait 1 but not with trait 2
PP.H2.abf	0.0468244133445686	association with trait 2 but not with trait 1
PP.H3.abf	0.111130501900955	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.841982624686278	association with trait 1 and trait 2, one shared SNP

pvalues in coloclization
		note
nsnps	1986	NA
PP.H0.abf	0.00027523983668062	no association with either trait
PP.H1.abf	7.3056726176907e-05	association with trait 1 but not with trait 2
PP.H2.abf	0.69977172359009	association with trait 2 but not with trait 1
PP.H3.abf	0.185625687327869	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.114254292519184	association with trait 1 and trait 2, one shared SNP

red blood cell count

LV82

pvalues in coloclization
		note
nsnps	2597	NA
PP.H0.abf	0.000552401715551205	no association with either trait
PP.H1.abf	0.000142894163020125	association with trait 1 but not with trait 2
PP.H2.abf	0.767428590867418	association with trait 2 but not with trait 1
PP.H3.abf	0.198483489352633	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.0333926239013786	association with trait 1 and trait 2, one shared SNP

pvalues in coloclization
		note
nsnps	2106	NA
PP.H0.abf	0.000534082743566863	no association with either trait
PP.H1.abf	0.000147333853948397	association with trait 1 but not with trait 2
PP.H2.abf	0.741967762385206	association with trait 2 but not with trait 1
PP.H3.abf	0.204628989484599	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.0527218315326792	association with trait 1 and trait 2, one shared SNP

pvalues in coloclization
		note
nsnps	1773	NA
PP.H0.abf	0.000532066325912198	no association with either trait
PP.H1.abf	0.000162142075422079	association with trait 1 but not with trait 2
PP.H2.abf	0.739156701624836	association with trait 2 but not with trait 1
PP.H3.abf	0.225215934691157	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.0349331552826738	association with trait 1 and trait 2, one shared SNP

pvalues in coloclization
		note
nsnps	1733	NA
PP.H0.abf	0.000536897512154383	no association with either trait
PP.H1.abf	0.000146989117164231	association with trait 1 but not with trait 2
PP.H2.abf	0.745867436555273	association with trait 2 but not with trait 1
PP.H3.abf	0.20415056040199	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.0492981164134186	association with trait 1 and trait 2, one shared SNP

white blood cell count

LV6

pvalues in coloclization
		note
nsnps	1692	NA
PP.H0.abf	2.42069422133774e-20	no association with either trait
PP.H1.abf	5.85832479206917e-21	association with trait 1 but not with trait 2
PP.H2.abf	0.752317669664185	association with trait 2 but not with trait 1
PP.H3.abf	0.18200280383898	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.0656795264968316	association with trait 1 and trait 2, one shared SNP

pvalues in coloclization
		note
nsnps	2527	NA
PP.H0.abf	0.664208360811174	no association with either trait
PP.H1.abf	0.205620211187087	association with trait 1 but not with trait 2
PP.H2.abf	0.0885804081235787	association with trait 2 but not with trait 1
PP.H3.abf	0.027407817633705	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.0141832022444551	association with trait 1 and trait 2, one shared SNP

LV119

pvalues in coloclization
		note
nsnps	1473	NA
PP.H0.abf	0.596714698368467	no association with either trait
PP.H1.abf	0.305368562821701	association with trait 1 but not with trait 2
PP.H2.abf	0.0522979566455676	association with trait 2 but not with trait 1
PP.H3.abf	0.0267445889878615	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.0188741931764027	association with trait 1 and trait 2, one shared SNP

pvalues in coloclization
		note
nsnps	514	NA
PP.H0.abf	0.926358700221567	no association with either trait
PP.H1.abf	0.0498079011502066	association with trait 1 but not with trait 2
PP.H2.abf	0.0206041219700063	association with trait 2 but not with trait 1
PP.H3.abf	0.00110570655025839	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.00212357010796139	association with trait 1 and trait 2, one shared SNP

white blood cell count

LV47

pvalues in coloclization
		note
nsnps	433	NA
PP.H0.abf	1.15537632748376e-06	no association with either trait
PP.H1.abf	6.97799012112253e-08	association with trait 1 but not with trait 2
PP.H2.abf	0.899698351085931	association with trait 2 but not with trait 1
PP.H3.abf	0.0542920116470383	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.0460084121108017	association with trait 1 and trait 2, one shared SNP

pvalues in coloclization
		note
nsnps	807	NA
PP.H0.abf	1.75215744541055e-07	no association with either trait
PP.H1.abf	1.83287532668058e-08	association with trait 1 but not with trait 2
PP.H2.abf	0.850469002019758	association with trait 2 but not with trait 1
PP.H3.abf	0.088904189531708	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.0606266149040363	association with trait 1 and trait 2, one shared SNP

Colocalization – 200kb

SNP selection: 1. Chose the SNPs in the info table. 2. For each SNP, the region used in colocalization analysis is between [pos-100kb, pos+100kb]. 3. All SNPs in this region are included in alalysis.

BMI

LV27

PPs in coloclization analysis
		note
nsnps	207	NA
PP.H0.abf	1.31495360908805e-145	no association with either trait
PP.H1.abf	5.2049528767862e-147	association with trait 1 but not with trait 2
PP.H2.abf	0.811794228353762	association with trait 2 but not with trait 1
PP.H3.abf	0.0319768491954829	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.156228922450753	association with trait 1 and trait 2, one shared SNP

PPs in coloclization analysis
		note
nsnps	177	NA
PP.H0.abf	6.75116568372173e-09	no association with either trait
PP.H1.abf	5.40587281938705e-10	association with trait 1 but not with trait 2
PP.H2.abf	0.564285233583145	association with trait 2 but not with trait 1
PP.H3.abf	0.0447931895827552	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.390921569542346	association with trait 1 and trait 2, one shared SNP

LV76

PPs in coloclization analysis
		note
nsnps	305	NA
PP.H0.abf	0.043354512024562	no association with either trait
PP.H1.abf	0.00365449985190367	association with trait 1 but not with trait 2
PP.H2.abf	0.820968372095008	association with trait 2 but not with trait 1
PP.H3.abf	0.0691393439964714	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.062883272032055	association with trait 1 and trait 2, one shared SNP

PPs in coloclization analysis
		note
nsnps	198	NA
PP.H0.abf	0.049007904441049	no association with either trait
PP.H1.abf	0.0227480522824381	association with trait 1 but not with trait 2
PP.H2.abf	0.519650730686723	association with trait 2 but not with trait 1
PP.H3.abf	0.241039290569512	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.167554022020278	association with trait 1 and trait 2, one shared SNP

LDL

LV125

PPs in coloclization analysis
		note
nsnps	159	NA
PP.H0.abf	1.65803672342948e-09	no association with either trait
PP.H1.abf	1.12233006918158e-10	association with trait 1 but not with trait 2
PP.H2.abf	0.620560571711587	association with trait 2 but not with trait 1
PP.H3.abf	0.0416681613863381	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.337771265131805	association with trait 1 and trait 2, one shared SNP

PPs in coloclization analysis
		note
nsnps	280	NA
PP.H0.abf	8.66392262651223e-31	no association with either trait
PP.H1.abf	7.94897609482024e-31	association with trait 1 but not with trait 2
PP.H2.abf	0.499148954943497	association with trait 2 but not with trait 1
PP.H3.abf	0.457916269344939	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.0429347757115665	association with trait 1 and trait 2, one shared SNP

PPs in coloclization analysis
		note
nsnps	274	NA
PP.H0.abf	8.82484066440544e-31	no association with either trait
PP.H1.abf	8.09234684377751e-31	association with trait 1 but not with trait 2
PP.H2.abf	0.508419821490731	association with trait 2 but not with trait 1
PP.H3.abf	0.466193743656564	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.0253864348527016	association with trait 1 and trait 2, one shared SNP

lymphocyte count

LV26

pvalues in coloclization
		note
nsnps	804	NA
PP.H0.abf	0.845766444165032	no association with either trait
PP.H1.abf	0.110794734986824	association with trait 1 but not with trait 2
PP.H2.abf	0.0342314385498326	association with trait 2 but not with trait 1
PP.H3.abf	0.00447956354621296	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.00472781875209829	association with trait 1 and trait 2, one shared SNP

pvalues in coloclization
		note
nsnps	586	NA
PP.H0.abf	0.681501963451757	no association with either trait
PP.H1.abf	0.274861949970861	association with trait 1 but not with trait 2
PP.H2.abf	0.0202160229511401	association with trait 2 but not with trait 1
PP.H3.abf	0.0081382023353829	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.0152818612908584	association with trait 1 and trait 2, one shared SNP

platelet count

LV49

pvalues in coloclization
		note
nsnps	523	NA
PP.H0.abf	0.466538003013372	no association with either trait
PP.H1.abf	0.105846694770366	association with trait 1 but not with trait 2
PP.H2.abf	0.328747014945193	association with trait 2 but not with trait 1
PP.H3.abf	0.0745607953782274	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.0243074918928418	association with trait 1 and trait 2, one shared SNP

pvalues in coloclization
		note
nsnps	474	NA
PP.H0.abf	8.1176654299053e-08	no association with either trait
PP.H1.abf	9.36336316698695e-08	association with trait 1 but not with trait 2
PP.H2.abf	0.099414413602958	association with trait 2 but not with trait 1
PP.H3.abf	0.11388336740316	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.786702044183596	association with trait 1 and trait 2, one shared SNP

pvalues in coloclization
		note
nsnps	945	NA
PP.H0.abf	0.000323941065497684	no association with either trait
PP.H1.abf	5.35834117434399e-05	association with trait 1 but not with trait 2
PP.H2.abf	0.823567437081277	association with trait 2 but not with trait 1
PP.H3.abf	0.136187235765739	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.0398678026757416	association with trait 1 and trait 2, one shared SNP

red blood cell count

LV82

pvalues in coloclization
		note
nsnps	932	NA
PP.H0.abf	0.862160613876924	no association with either trait
PP.H1.abf	0.095492523950811	association with trait 1 but not with trait 2
PP.H2.abf	0.0345160944404618	association with trait 2 but not with trait 1
PP.H3.abf	0.00381897538892352	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.00401179234287936	association with trait 1 and trait 2, one shared SNP

pvalues in coloclization
		note
nsnps	1007	NA
PP.H0.abf	0.791255162135573	no association with either trait
PP.H1.abf	0.160499440153454	association with trait 1 but not with trait 2
PP.H2.abf	0.0352370317017519	association with trait 2 but not with trait 1
PP.H3.abf	0.00714166817009981	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.0058666978391218	association with trait 1 and trait 2, one shared SNP

pvalues in coloclization
		note
nsnps	766	NA
PP.H0.abf	0.842378151311792	no association with either trait
PP.H1.abf	0.121767261460728	association with trait 1 but not with trait 2
PP.H2.abf	0.0270036088172732	association with trait 2 but not with trait 1
PP.H3.abf	0.00389846721795865	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.00495251119224877	association with trait 1 and trait 2, one shared SNP

pvalues in coloclization
		note
nsnps	845	NA
PP.H0.abf	0.794937633920837	no association with either trait
PP.H1.abf	0.163102621759243	association with trait 1 but not with trait 2
PP.H2.abf	0.0291504707276119	association with trait 2 but not with trait 1
PP.H3.abf	0.00597416012226824	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.00683511347003905	association with trait 1 and trait 2, one shared SNP

white blood cell count

LV6

pvalues in coloclization
		note
nsnps	790	NA
PP.H0.abf	2.55244831056944e-20	no association with either trait
PP.H1.abf	4.42587376275609e-21	association with trait 1 but not with trait 2
PP.H2.abf	0.793264984903673	association with trait 2 but not with trait 1
PP.H3.abf	0.137480668567092	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.0692543465292343	association with trait 1 and trait 2, one shared SNP

pvalues in coloclization
		note
nsnps	925	NA
PP.H0.abf	0.827099341301274	no association with either trait
PP.H1.abf	0.123460953436092	association with trait 1 but not with trait 2
PP.H2.abf	0.0379056341098084	association with trait 2 but not with trait 1
PP.H3.abf	0.00565228464418128	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.00588178650864373	association with trait 1 and trait 2, one shared SNP

LV119

pvalues in coloclization
		note
nsnps	558	NA
PP.H0.abf	2.59596880772956e-17	no association with either trait
PP.H1.abf	7.9810396381393e-18	association with trait 1 but not with trait 2
PP.H2.abf	0.73055366654872	association with trait 2 but not with trait 1
PP.H3.abf	0.224556336071001	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.0448899973802794	association with trait 1 and trait 2, one shared SNP

pvalues in coloclization
		note
nsnps	301	NA
PP.H0.abf	0.962205966613379	no association with either trait
PP.H1.abf	0.0231789040116609	association with trait 1 but not with trait 2
PP.H2.abf	0.013173262987228	association with trait 2 but not with trait 1
PP.H3.abf	0.000316209517610261	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.00112565687012208	association with trait 1 and trait 2, one shared SNP

white blood cell count

LV47

pvalues in coloclization
		note
nsnps	173	NA
PP.H0.abf	1.15814920856251e-06	no association with either trait
PP.H1.abf	3.90974515028206e-08	association with trait 1 but not with trait 2
PP.H2.abf	0.901857588140806	association with trait 2 but not with trait 1
PP.H3.abf	0.0303776514611794	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.0677635631513558	association with trait 1 and trait 2, one shared SNP

pvalues in coloclization
		note
nsnps	288	NA
PP.H0.abf	1.84200928897656e-07	no association with either trait
PP.H1.abf	1.29379118273315e-08	association with trait 1 but not with trait 2
PP.H2.abf	0.894081636109299	association with trait 2 but not with trait 1
PP.H3.abf	0.0627553770637426	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.0431627896881166	association with trait 1 and trait 2, one shared SNP

Results - pval < 5e-8 & association test covariants: 10 PCs

Summary table

Info table

The suffix ’_assoc’ here means that results are from factor ~ SNP + genotype pcs association test. The suffix ’_gwas’ here means results are from original GWAS results files. For EUR.CD, EUR.IBD, EUR.UC, the effectsize_gwas here means ‘ln(OR)’, for others, it means ‘beta’.

Enrichment analysis

ORA: For each factor, I sorted the genes in 1 by their loadings, taking the top 25% as the gene set. The function database here are Reactome pathway; Disgenet + GLAD4U + OMIM disease dataset; geneontology biological process. The reference set affy hugene 2 0 st v1. Minimum number of genes for a category is 5, maximum number of genes for a category is 2000(default settings). All categories that have fdr<0.01 are listed.

GSEA: For each factor, I used all genes (zero loading genes included), taking the gene loadings as their gene score in GSEA. The function database is Reactome pathway.

BMI-LV3-Reactome BMI-LV3-GO BMI-LV3-phenotype BMI-LV3-disease-Disgenet

BMI-LV27-Reactome BMI-LV27-GO BMI-LV27-phenotype BMI-LV27-disease-Disgenet

BMI-LV76-Reactome BMI-LV76-GO BMI-LV76-phenotype BMI-LV76-disease-Disgenet

RBC-LV82-Reactome RBC-LV82-GO RBC-LV82-phenotype RBC-LV82-disease-Disgenet

Asthma-LV68-Reactome Asthma-LV68-GO Asthma-LV68-phenotype Asthma-LV68-disease-Disgenet

WBC-LV119-Reactome WBC-LV119-GO WBC-LV119-phenotype WBC-LV119-disease-Disgenet

Lymphocyte-LV23-Reactome Lymphocyte-LV23-GO Lymphocyte-LV23-phenotype Lymphocyte-LV23-disease-Disgenet

Lymphocyte-LV78 Lymphocyte-LV78-GO Lymphocyte-LV78-phenotype Lymphocyte-LV78-disease-Disgenet

GSEA: For each factor, I used all genes (zero loading genes included), taking the gene loadings as their gene score in GSEA. For those loadings = 0, I assigned a random number from normal distribution N(0,1e-5) to it to avoid ties. The function database is Reactome pathway.

Effect size plots

BMI

Eosinophil count

Crohn’s disease

IBD

Ulcerative colitist

granulocyte count

lymphocyte count

myeloid white cell count

neutrophil count

None of the LVs have >1 SNPs at FDR<0.2.

plt

None of the LVs have >1 SNPs at FDR<0.2.

rbc

asthma

wbc

effect size plots – more SNPs

For some promising trait-factor pairs (i.e. BMI-LV3, LV27,LV76, RBC-LV82, Asthma-LV68, WBC-LV119, Lymphocyte-LV23, LV78), I relaxed the fdr threshold of the SNPs that used to make effect size plots(from 0.2 to 0.3/0.5)

I also made a plot to show the the distribution of the SNPs’ fdr.

BMI

Crohn’s disease

The CD~lv88 is not very promising pairs when considering SNPs at fdr<0.2, but the fitting result at fdr <0.5 is better than the former result . So I post the plots here too.

lymphocyte count

red blood cell count

The rbc~lv42 and rbc~lv59 pairs are not very promising pairs when considering SNPs at fdr<0.2, but the fitting results at fdr <0.5 are better than the former results. So I post the plots here too.

asthma

white blood cell count

Effect size plots - checking the reverse causality

Some pair show p < 0.05, the result may be driven by the possible causal effect of LV -> trait. To test this, I removed the SNPs that are associated with LVs at FDR < 0.2 and made the plots again.

BMI

Crohn’s disease

The CD~lv88 is not very promising pairs when considering SNPs at fdr<0.2, but the fitting result at fdr <0.5 is better than the former result . So I post the plots here too.

lymphocyte count

red blood cell count

The rbc~lv42 and rbc~lv59 pairs are not very promising pairs when considering SNPs at fdr<0.2, but the fitting results at fdr <0.5 are better than the former results. So I post the plots here too.

asthma

white blood cell count

Results - pval < 5e-8 & association test covariants: 10 PCs + GTEx:Sequencing platform,Sequencing protocol,Sex

Summary table

Info table

The suffix ’_assoc’ here means that results are from factor ~ SNP + genotype pcs association test. The suffix ’_gwas’ here means results are from original GWAS results files. For EUR.CD, EUR.IBD, EUR.UC, the effectsize_gwas here means ‘ln(OR)’, for others, it means ‘beta’.

Effect size plots

BMI

Eosinophil count

Crohn’s disease

IBD

Ulcerative colitist

granulocyte count

None of the LVs have >1 SNPs at FDR<0.2.

lymphocyte count

myeloid white cell count

neutrophil count

plt

rbc

T2D

None of the LVs have >1 SNPs at FDR<0.2.

asthma

wbc

Effect size plots- more SNPs

For some promising trait-factor pairs , I relaxed the fdr threshold of the SNPs that used to make effect size plots(from 0.2 to 0.3/0.5)

BMI

The BMI~lv90 is not very promising pairs when considering SNPs at fdr<0.2, but the fitting result at fdr <0.5 is better than the former result . So I post the plots here too.

lymphocyte count

red blood cell count

asthma

white blood cell count

Effect size plots - checking the reverse causality

Some pair show p < 0.05, the result may be driven by the possible causal effect of LV -> trait. To test this, I removed the SNPs that are associated with LVs at FDR < 0.2 and made the plots again.

BMI

lymphocyte count

red blood cell count

asthma

white blood cell count

sessionInfo()

R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5        rstudioapi_0.11   whisker_0.3-2     knitr_1.30       
 [5] magrittr_1.5      R6_2.4.1          rlang_0.4.8       highr_0.8        
 [9] stringr_1.4.0     tools_3.6.1       DT_0.15           xfun_0.18        
[13] git2r_0.26.1      crosstalk_1.1.0.1 htmltools_0.5.0   ellipsis_0.3.1   
[17] rprojroot_1.3-2   yaml_2.2.1        digest_0.6.25     tibble_3.0.3     
[21] lifecycle_0.2.0   crayon_1.3.4      later_1.1.0.1     htmlwidgets_1.5.2
[25] vctrs_0.3.4       promises_1.1.1    fs_1.5.0          glue_1.4.2       
[29] evaluate_0.14     rmarkdown_1.13    stringi_1.5.3     compiler_3.6.1   
[33] pillar_1.4.6      backports_1.1.10  jsonlite_1.7.1    httpuv_1.5.1     
[37] pkgconfig_2.0.3

absolute	relative
/project2/xinhe/xsun/website/factor_analysis/output/sum_sep_tog_pval_pliercanon_wgpc_ldcl_d1k_ageco.rdata	output/sum_sep_tog_pval_pliercanon_wgpc_ldcl_d1k_ageco.rdata
/project2/xinhe/xsun/website/factor_analysis/output/info_pval5e8_pliercanon_ld_d1k_ageco.rdata	output/info_pval5e8_pliercanon_ld_d1k_ageco.rdata
/project2/xinhe/xsun/website/factor_analysis/output/lv27top025_pliercanon.rdata	output/lv27top025_pliercanon.rdata
/project2/xinhe/xsun/website/factor_analysis/output/lv76top025_pliercanon.rdata	output/lv76top025_pliercanon.rdata
/project2/xinhe/xsun/website/factor_analysis/output/lv90top025_pliercanon.rdata	output/lv90top025_pliercanon.rdata
/project2/xinhe/xsun/website/factor_analysis/output/lv49top025_pliercanon.rdata	output/lv49top025_pliercanon.rdata
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/project2/xinhe/xsun/website/factor_analysis/output/sum_sep_tog_pval_pliercanon_wgpc_ldcl_d1k.rdata	output/sum_sep_tog_pval_pliercanon_wgpc_ldcl_d1k.rdata
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catalog_gwas_pliercanon_sep_ldclumping_r01d1000

XSun

2020-10-14

Introduction

Material and Methods

Snps after filter

Results - pval < 5e-8 & association test covariants: 10 PCs + GTEx:Sequencing platform,Sequencing protocol,Sex + AGE

Summary table

Info table

Enrichment analysis

Effect size plots

Asthma_2

BMI

Eosinophil count

Crohn’s disease

IBD

Ulcerative colitist

LDL

granulocyte count

lymphocyte count

myeloid white cell count

neutrophil count

plt

rbc

T2D_2

T2D

asthma

wbc

WHR

Effect size plots - checking the reverse causality

BMI

LDL

lymphocyte count

platelet count

red blood cell count

asthma

white blood cell count

WHR

QQplots

BMI

LDL

platelet count

red blood cell count

asthma

white blood cell count

WHR

Colocalization – 200kb

BMI

lymphocyte count

platelet count

red blood cell count

white blood cell count

white blood cell count

Colocalization – 200kb

BMI

LDL

lymphocyte count

platelet count

red blood cell count

white blood cell count

white blood cell count

Results - pval < 5e-8 & association test covariants: 10 PCs

Summary table

Info table

Enrichment analysis

Effect size plots

BMI

Eosinophil count

Crohn’s disease

IBD

Ulcerative colitist

granulocyte count

lymphocyte count

myeloid white cell count

neutrophil count

plt

rbc

asthma

wbc

effect size plots – more SNPs